A Flexible Software-Hardware Framework for Brain EEG Multiple Artifact Identification

Abstract

This chapter presents an energy efficient and flexible multichannel Electroencephalogram (EEG) artifact identification network and its hardware using depthwise and separable convolutional neural networks (DS-CNN). EEG signals are recordings of the brain activities. The EEG recordings that are not originated from cerebral activities are termed as artifacts. Our proposed model does not need expert knowledge for feature extraction or pre-processing of EEG data and has a very efficient architecture implementable on mobile devices. The proposed network can be reconfigured for any number of EEG channel and artifact classes. Experiments were done with the proposed model with the goal of maximizing the identification accuracy while minimizing the weight parameters and required number of operations.